Economics at your fingertips  

Modeling Credit Risk with Hidden Markov Default Intensity

Feng-Hui Yu (), Jiejun Lu (), Jia-Wen Gu () and Wai-Ki Ching ()
Additional contact information
Feng-Hui Yu: The University of Hong Kong
Jiejun Lu: Harvard John A. Paulson School of Engineering and Applied Sciences
Jia-Wen Gu: Southern University of Science and Technology
Wai-Ki Ching: The University of Hong Kong

Computational Economics, 2019, vol. 54, issue 3, No 15, 1213-1229

Abstract: Abstract This paper investigates the modeling of credit default under an interactive reduced-form intensity-based model based on the Hidden Markov setting proposed in Yu et al. (Quant Finance 7(5):781–794, 2017). The intensities of defaults are determined by the hidden economic states which are governed by a Markov chain, as well as the past defaults. We estimate the parameters in the default intensity by using Expectation–Maximization algorithm with real market data under three different practical default models. Applications to pricing of credit default swap (CDS) is also discussed. Numerical experiments are conducted to compare the results under our models with real recession periods in US. The results demonstrate that our model is able to capture the hidden features and simulate credit default risks which are critical in risk management and the extracted hidden economic states are consistent with the real market data. In addition, we take pricing CDS as an example to illustrate the sensitivity analysis.

Keywords: Credit default swap (CDS); Credit risk; Expectation–maximization (EM) algorithm; Intensity models (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-018-9869-7

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla ().

Page updated 2020-04-23
Handle: RePEc:kap:compec:v:54:y:2019:i:3:d:10.1007_s10614-018-9869-7